



'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class 							a__( snake_case__     ):
			a_	: torch.FloatTensor
			a_	: torch.FloatTensor
			a_	: Optional[torch.FloatTensor]									=      None





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: List[Any]									=      2






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 100					,   _UpperCAmelCase = 1.007					,   _UpperCAmelCase = 80					,   _UpperCAmelCase = 0.05					,   _UpperCAmelCase = 50					,   )							->							int:
					# standard deviation of the initial noise distribution
					snake_case__	     =sigma_max

					# setable values
					snake_case__	     =None
					snake_case__	     =None
					snake_case__	     =None  # sigma(t_i)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							torch.FloatTensor:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Any:
					snake_case__	     =num_inference_steps
					snake_case__	     =np.arange(0					,   self.num_inference_steps						)[::-1].copy()
					snake_case__	     =torch.from_numpy(_UpperCAmelCase						).to(_UpperCAmelCase						)
					snake_case__	     =[
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					snake_case__	     =torch.tensor(_UpperCAmelCase					,   dtype=torch.floataa					,   device=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[torch.FloatTensor, float]:
					if self.config.s_min <= sigma <= self.config.s_max:
							snake_case__	     =min(self.config.s_churn / self.num_inference_steps					,   2**0.5 - 1						)
					else:
							snake_case__	     =0

					# sample eps ~ N(0, S_noise^2 * I)
					snake_case__	     =self.config.s_noise * randn_tensor(sample.shape					,   generator=_UpperCAmelCase						).to(sample.device						)
					snake_case__	     =sigma + gamma * sigma
					snake_case__	     =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = True					,   )							->							Union[KarrasVeOutput, Tuple]:
					snake_case__	     =sample_hat + sigma_hat * model_output
					snake_case__	     =(sample_hat - pred_original_sample) / sigma_hat
					snake_case__	     =sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
							return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=_UpperCAmelCase					,   derivative=_UpperCAmelCase					,   pred_original_sample=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = True					,   )							->							Union[KarrasVeOutput, Tuple]:
					snake_case__	     =sample_prev + sigma_prev * model_output
					snake_case__	     =(sample_prev - pred_original_sample) / sigma_prev
					snake_case__	     =sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
							return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=_UpperCAmelCase					,   derivative=_UpperCAmelCase					,   pred_original_sample=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					raise NotImplementedError()









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

class 							a__( snake_case__     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							None:
					warnings.warn(
					    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use GLPNImageProcessor instead.'					,   _UpperCAmelCase					,   )
					super().__init__(*_UpperCAmelCase					,   **_UpperCAmelCase						)




